rmontanana/BayesNet
1
0
Fork 0
Code Issues 2 Pull Requests 1 Actions Packages Projects Releases 6 Wiki Activity

Fix some mistakes in tensors treatment

Browse Source
This commit is contained in:
Ricardo Montañana Gómez
2023-07-26 01:39:01 +02:00
parent be06e475f0
commit 099b4bea09
18 changed files with 255 additions and 72 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
sample/CMakeLists.txt
View File

@@ -3,5 +3,5 @@ include_directories(${BayesNet_SOURCE_DIR}/src/BayesNet)
include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
include_directories(${BayesNet_SOURCE_DIR}/lib/argparse/include)
add_executable(BayesNetSample sample.cc)
add_executable(BayesNetSample sample.cc ${BayesNet_SOURCE_DIR}/src/Platform/Folding.cc)
target_link_libraries(BayesNetSample BayesNet ArffFiles mdlp "${TORCH_LIBRARIES}")

96
sample/sample.cc
View File

@@ -1,4 +1,5 @@
#include <iostream>
#include <torch/torch.h>
#include <string>
#include <thread>
#include <map>
@@ -12,6 +13,7 @@
#include "SPODE.h"
#include "AODE.h"
#include "TAN.h"
#include "Folding.h"
using namespace std;
@@ -42,6 +44,21 @@ bool file_exists(const std::string& name)
return false;
}
}
pair<vector<vector<int>>, vector<int>> extract_indices(vector<int> indices, vector<vector<int>> X, vector<int> y)
{
vector<vector<int>> Xr;
vector<int> yr;
for (int col = 0; col < X.size(); ++col) {
Xr.push_back(vector<int>());
}
for (auto index : indices) {
for (int col = 0; col < X.size(); ++col) {
Xr[col].push_back(X[col][index]);
}
yr.push_back(y[index]);
}
return { Xr, yr };
}
int main(int argc, char** argv)
{
@@ -60,7 +77,7 @@ int main(int argc, char** argv)
valid_datasets.push_back(dataset.first);
}
argparse::ArgumentParser program("BayesNetSample");
program.add_argument("-f", "--file")
program.add_argument("-d", "--dataset")
.help("Dataset file name")
.action([valid_datasets](const std::string& value) {
if (find(valid_datasets.begin(), valid_datasets.end(), value) != valid_datasets.end()) {
@@ -83,14 +100,37 @@ int main(int argc, char** argv)
throw runtime_error("Model must be one of {AODE, KDB, SPODE, TAN}");
}
);
bool class_last;
program.add_argument("--discretize").help("Discretize input dataset").default_value(false).implicit_value(true);
program.add_argument("--stratified").help("If Stratified KFold is to be done").default_value(false).implicit_value(true);
program.add_argument("--tensors").help("Use tensors to store samples").default_value(false).implicit_value(true);
program.add_argument("-f", "--folds").help("Number of folds").default_value(5).scan<'i', int>().action([](const string& value) {
try {
auto k = stoi(value);
if (k < 2) {
throw runtime_error("Number of folds must be greater than 1");
}
return k;
}
catch (const runtime_error& err) {
throw runtime_error(err.what());
}
catch (...) {
throw runtime_error("Number of folds must be an integer");
}});
program.add_argument("-s", "--seed").help("Random seed").default_value(-1).scan<'i', int>();
bool class_last, stratified, tensors;
string model_name, file_name, path, complete_file_name;
int nFolds, seed;
try {
program.parse_args(argc, argv);
file_name = program.get<string>("file");
file_name = program.get<string>("dataset");
path = program.get<string>("path");
model_name = program.get<string>("model");
complete_file_name = path + file_name + ".arff";
stratified = program.get<bool>("stratified");
tensors = program.get<bool>("tensors");
nFolds = program.get<int>("folds");
seed = program.get<int>("seed");
class_last = datasets[file_name];
if (!file_exists(complete_file_name)) {
throw runtime_error("Data File " + path + file_name + ".arff" + " does not exist");
@@ -144,5 +184,55 @@ int main(int argc, char** argv)
file.close();
cout << "Graph saved in " << model_name << "_" << file_name << ".dot" << endl;
cout << "dot -Tpng -o " + dot_file + ".png " + dot_file + ".dot " << endl;
string stratified_string = stratified ? " Stratified" : "";
cout << nFolds << " Folds" << stratified_string << " Cross validation" << endl;
cout << "==========================================" << endl;
torch::Tensor Xt = torch::zeros({ static_cast<int>(Xd.size()), static_cast<int>(Xd[0].size()) }, torch::kInt32);
torch::Tensor yt = torch::tensor(y, torch::kInt32);
for (int i = 0; i < features.size(); ++i) {
Xt.index_put_({ i, "..." }, torch::tensor(Xd[i], torch::kInt32));
}
float total_score = 0, total_score_train = 0, score_train, score_test;
Fold* fold;
if (stratified)
fold = new StratifiedKFold(nFolds, y, seed);
else
fold = new KFold(nFolds, y.size(), seed);
for (auto i = 0; i < nFolds; ++i) {
auto [train, test] = fold->getFold(i);
cout << "Fold: " << i + 1 << endl;
if (tensors) {
cout << "Xt shape: " << Xt.sizes() << endl;
cout << "yt shape: " << yt.sizes() << endl;
auto ttrain = torch::tensor(train, torch::kInt64);
auto ttest = torch::tensor(test, torch::kInt64);
torch::Tensor Xtraint = torch::index_select(Xt, 1, ttrain);
torch::Tensor ytraint = yt.index({ ttrain });
torch::Tensor Xtestt = torch::index_select(Xt, 1, ttest);
torch::Tensor ytestt = yt.index({ ttest });
cout << "Train: " << Xtraint.size(0) << " x " << Xtraint.size(1) << " " << ytraint.size(0) << endl;
cout << "Test : " << Xtestt.size(0) << " x " << Xtestt.size(1) << " " << ytestt.size(0) << endl;
clf->fit(Xtraint, ytraint, features, className, states);
score_train = clf->score(Xtraint, ytraint);
score_test = clf->score(Xtestt, ytestt);
} else {
auto [Xtrain, ytrain] = extract_indices(train, Xd, y);
auto [Xtest, ytest] = extract_indices(test, Xd, y);
cout << "Train: " << Xtrain.size() << " x " << Xtrain[0].size() << " " << ytrain.size() << endl;
cout << "Test : " << Xtest.size() << " x " << Xtest[0].size() << " " << ytest.size() << endl;
clf->fit(Xtrain, ytrain, features, className, states);
score_train = clf->score(Xtrain, ytrain);
score_test = clf->score(Xtest, ytest);
}
total_score_train += score_train;
total_score += score_test;
cout << "Score Train: " << score_train << endl;
cout << "Score Test : " << score_test << endl;
// cout << "-------------------------------------------------------------------------------" << endl;
// total_score += score_value;
}
cout << "**********************************************************************************" << endl;
cout << "Average Score Train: " << total_score_train / nFolds << endl;
cout << "Average Score Test : " << total_score / nFolds << endl;
return 0;
}